






import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Dict							       = None
				_UpperCamelCase					:       str							       = BloomTokenizerFast
				_UpperCamelCase					:       int							       = BloomTokenizerFast
				_UpperCamelCase					:       List[str]							       = True
				_UpperCamelCase					:       Union[str, Any]							       = False
				_UpperCamelCase					:       int							       = "tokenizer_file"
				_UpperCamelCase					:       str							       = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}


				def 						__a					(					self      ):
											super().setUp()
											_lowercase							:   List[str] 							=   BloomTokenizerFast.from_pretrained('bigscience/tokenizer'      )
											tokenizer.save_pretrained(self.tmpdirname      )


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											kwargs.update(self.special_tokens_map      )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.get_rust_tokenizer()

											_lowercase							:   Any 							=   ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											_lowercase							:   str 							=   [[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

											_lowercase							:   Dict 							=   tokenizer.batch_encode_plus(_lowerCAmelCase      )['input_ids']
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )

											_lowercase							:   Optional[int] 							=   tokenizer.batch_decode(_lowerCAmelCase      )
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase=6      ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																									_lowercase							:   Any 							=   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase ,						**_lowerCAmelCase      )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									_lowercase							:   int 							=   'This is a simple input'
																									_lowercase							:   Union[str, Any] 							=   ['This is a simple input 1', 'This is a simple input 2']
																									_lowercase							:   Union[str, Any] 							=   ('This is a simple input', 'This is a pair')
																									_lowercase							:   Optional[int] 							=   [
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(_lowerCAmelCase ,						max_length=_lowerCAmelCase      )
																																tokenizer_r.encode_plus(_lowerCAmelCase ,						max_length=_lowerCAmelCase      )

																																tokenizer_r.batch_encode_plus(_lowerCAmelCase ,						max_length=_lowerCAmelCase      )
																																tokenizer_r.encode(_lowerCAmelCase ,						max_length=_lowerCAmelCase      )
																																tokenizer_r.batch_encode_plus(_lowerCAmelCase ,						max_length=_lowerCAmelCase      )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'      )

																									_lowercase							:   Any 							=   None  # Hotfixing padding = None
																									self.assertRaises(_lowerCAmelCase ,						tokenizer_r.encode ,						_lowerCAmelCase ,						max_length=_lowerCAmelCase ,						padding='max_length'      )

																									# Simple input
																									self.assertRaises(_lowerCAmelCase ,						tokenizer_r.encode_plus ,						_lowerCAmelCase ,						max_length=_lowerCAmelCase ,						padding='max_length'      )

																									# Simple input
																									self.assertRaises(
																									    _lowerCAmelCase ,						tokenizer_r.batch_encode_plus ,						_lowerCAmelCase ,						max_length=_lowerCAmelCase ,						padding='max_length' ,						)

																									# Pair input
																									self.assertRaises(_lowerCAmelCase ,						tokenizer_r.encode ,						_lowerCAmelCase ,						max_length=_lowerCAmelCase ,						padding='max_length'      )

																									# Pair input
																									self.assertRaises(_lowerCAmelCase ,						tokenizer_r.encode_plus ,						_lowerCAmelCase ,						max_length=_lowerCAmelCase ,						padding='max_length'      )

																									# Pair input
																									self.assertRaises(
																									    _lowerCAmelCase ,						tokenizer_r.batch_encode_plus ,						_lowerCAmelCase ,						max_length=_lowerCAmelCase ,						padding='max_length' ,						)


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   self.get_rust_tokenizer()
											_lowercase							:   List[Any] 							=   load_dataset('xnli' ,						'all_languages' ,						split='test' ,						streaming=_lowerCAmelCase      )

											_lowercase							:   Union[str, Any] 							=   next(iter(_lowerCAmelCase      )      )['premise']  # pick up one data
											_lowercase							:   Optional[int] 							=   list(sample_data.values()      )

											_lowercase							:   str 							=   list(map(tokenizer.encode ,						_lowerCAmelCase      )      )
											_lowercase							:   Union[str, Any] 							=   [tokenizer.decode(_lowerCAmelCase ,						clean_up_tokenization_spaces=_lowerCAmelCase      ) for x in output_tokens]
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					self      ):
											# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
											# any sequence length constraints. This test of the parent class will fail since it relies on the
											# maximum sequence length of the positoonal embeddings.
											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map      ) ,						1      )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()      )[0]      ) ,						1      )





import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
     import jax
@require_flax
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				FlaxAutoencoderKL







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      4
    UpperCamelCase			  =      3
    UpperCamelCase			  =      (32, 32)

    UpperCamelCase			  =      jax.random.PRNGKey(0				)
    UpperCamelCase			  =      jax.random.uniform(lowerCamelCase_   ,   ((batch_size, num_channels) + sizes)				)

    return {"sample": image, "prng_key": prng_key}







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      {
        """block_out_channels""": [32, 64],
        """in_channels""": 3,
        """out_channels""": 3,
        """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
        """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
        """latent_channels""": 4,
    }
    UpperCamelCase			  =      self.dummy_input
    return init_dict, inputs_dict
